import time
import os
import numpy as np
from itertools import chain
import torch
from tensorboardX import SummaryWriter

from utils.separated_buffer import SeparatedReplayBuffer
from utils.util import update_linear_schedule


def _t2n(x):
    return x.detach().cpu().numpy()


class Runner(object):
    def __init__(self, config):
        self.all_args = config["all_args"]
        self.envs = config["envs"]
        self.eval_envs = config["eval_envs"]
        self.device = config["device"]
        self.num_agents = config["num_agents"]

        # parameters
        self.env_name = self.all_args.env_name
        self.algorithm_name = self.all_args.algorithm_name
        self.experiment_name = self.all_args.experiment_name
        self.use_centralized_V = self.all_args.use_centralized_V
        self.use_obs_instead_of_state = self.all_args.use_obs_instead_of_state
        self.num_env_steps = self.all_args.num_env_steps
        self.episode_length = self.all_args.episode_length
        self.n_rollout_threads = self.all_args.n_rollout_threads
        self.n_eval_rollout_threads = self.all_args.n_eval_rollout_threads
        self.use_linear_lr_decay = self.all_args.use_linear_lr_decay
        self.hidden_size = self.all_args.hidden_size
        self.use_render = self.all_args.use_render
        self.recurrent_N = self.all_args.recurrent_N

        # interval
        self.save_interval = self.all_args.save_interval
        self.use_eval = self.all_args.use_eval
        self.eval_interval = self.all_args.eval_interval
        self.log_interval = self.all_args.log_interval

        # dir
        self.model_dir = self.all_args.model_dir

        if self.use_render:
            import imageio

            self.run_dir = config["run_dir"]
            self.gif_dir = str(self.run_dir / "gifs")
            if not os.path.exists(self.gif_dir):
                os.makedirs(self.gif_dir)
            
            self.run_dir = config["run_dir"]
            self.log_dir = str(self.run_dir / "logs")
            if not os.path.exists(self.log_dir):
                os.makedirs(self.log_dir)
            self.writter = SummaryWriter(self.log_dir)
            self.save_dir = str(self.run_dir / "models")
            if not os.path.exists(self.save_dir):
                os.makedirs(self.save_dir)
        else:
            # if self.use_wandb:
            #     self.save_dir = str(wandb.run.dir)
            # else:
            self.run_dir = config["run_dir"]
            self.log_dir = str(self.run_dir / "logs")
            if not os.path.exists(self.log_dir):
                os.makedirs(self.log_dir)
            self.writter = SummaryWriter(self.log_dir)
            self.save_dir = str(self.run_dir / "models")
            if not os.path.exists(self.save_dir):
                os.makedirs(self.save_dir)
        from algorithms.algorithm.r_mappo import RMAPPO as TrainAlgo
        from algorithms.algorithm.rMAPPOPolicy import RMAPPOPolicy as Policy

        self.policy = []
        for agent_id in range(self.num_agents):
            share_observation_space = (
                self.envs.share_observation_space[agent_id]
                if self.use_centralized_V
                else self.envs.observation_space[agent_id]
            )
            # policy network
            po = Policy(
                self.all_args,
                self.envs.observation_space[agent_id],
                share_observation_space,
                self.envs.action_space[agent_id],
                device=self.device,
            )
            self.policy.append(po)


        if self.model_dir is not None:
            self.restore()

        self.trainer = []
        self.buffer = []
        for agent_id in range(self.num_agents):
            # algorithm
            tr = TrainAlgo(self.all_args, self.policy[agent_id], device=self.device)
            # buffer
            share_observation_space = (
                self.envs.share_observation_space[agent_id]
                if self.use_centralized_V
                else self.envs.observation_space[agent_id]
            )
            bu = SeparatedReplayBuffer(
                self.all_args,
                self.envs.observation_space[agent_id],
                share_observation_space,
                self.envs.action_space[agent_id]
            )
            self.buffer.append(bu)
            self.trainer.append(tr)


    def run(self):
        raise NotImplementedError

    def warmup(self):
        raise NotImplementedError

    def collect(self, step):
        raise NotImplementedError

    def insert(self, data):
        raise NotImplementedError

    @torch.no_grad()
    def compute(self):
        for agent_id in range(self.num_agents):
            self.trainer[agent_id].prep_rollout()
            next_value = self.trainer[agent_id].policy.get_values(
                self.buffer[agent_id].share_obs[-1],
                self.buffer[agent_id].rnn_states_critic[-1],
                self.buffer[agent_id].masks[-1],
            )
            next_value = _t2n(next_value)
            self.buffer[agent_id].compute_returns(next_value, self.trainer[agent_id].value_normalizer)

    def train(self):
        train_infos = []
        for agent_id in range(self.num_agents):
            self.trainer[agent_id].prep_training()
            train_info = self.trainer[agent_id].train(self.buffer[agent_id])
            train_infos.append(train_info)
            self.buffer[agent_id].after_update()

        return train_infos

    def save(self):
        for agent_id in range(self.num_agents):
            policy_actor = self.trainer[agent_id].policy.actor
            torch.save(
                policy_actor.state_dict(),
                str(self.save_dir) + "/actor_agent" + str(agent_id) + ".pt",
            )
            policy_critic = self.trainer[agent_id].policy.critic
            torch.save(
                policy_critic.state_dict(),
                str(self.save_dir) + "/critic_agent" + str(agent_id) + ".pt",
            )
        # for agent_id in range(self.num_agents):
        #     policy_actor = self.trainer[agent_id].policy.actor
        #     policy_critic = self.trainer[agent_id].policy.critic
        #     policy_actor_optimizer = self.trainer[agent_id].policy.actor_optimizer
        #     policy_critic_optimizer = self.trainer[agent_id].policy.critic_optimizer
        #     torch.save({"actor_agent":policy_actor, 
        #     "critic_agent":policy_critic, 
        #     "policy_actor_optimizer": policy_actor_optimizer, 
        #     "policy_critic_optimizer": policy_critic_optimizer}, 
        #     str(self.save_dir) + "/agent" + str(agent_id) + ".pt",)
            


    def restore(self):
        for agent_id in range(self.num_agents):
            policy_actor_state_dict = torch.load(str(self.model_dir) + "/actor_agent" + str(agent_id) + ".pt")
            self.policy[agent_id].actor.load_state_dict(policy_actor_state_dict)
            policy_critic_state_dict = torch.load(
                str(self.model_dir) + "/critic_agent" + str(agent_id) + ".pt"
            )
            self.policy[agent_id].critic.load_state_dict(policy_critic_state_dict)
        # for agent_id in range(self.num_agents):
        #     checkpoint = torch.load(str(self.model_dir) + "/agent" + str(agent_id) + ".pt")
        #     self.policy[agent_id].actor = checkpoint["actor_agent"]
        #     self.policy[agent_id].critic = checkpoint["critic_agent"]
        #     # self.policy[agent_id].actor_optimizer = checkpoint["policy_actor_optimizer"]
        #     # self.policy[agent_id].critic_optimizer = checkpoint["policy_critic_optimizer"]

    def log_train(self, train_infos, total_num_steps):
        # print("---")
        for agent_id in range(self.num_agents):
            for k, v in train_infos[agent_id].items():
                # print("k, v", k, v)
                agent_k = "agent%i/" % agent_id + k
                # if self.use_wandb:
                #     pass
                # wandb.log({agent_k: v}, step=total_num_steps)
                # else:
                self.writter.add_scalars(agent_k, {agent_k: v}, total_num_steps)

    def log_eval(self, rewards, total_num_steps):
        for agent_id in range(self.num_agents):
                for episode_id in range(self.n_eval_rollout_threads):
                        reward = rewards[episode_id][agent_id]
                        agent_k = "agent%i/" % agent_id + "eval_reward/"+str(episode_id)
                        self.writter.add_scalars(agent_k, {agent_k: reward},total_num_steps)

    def log_reward(self, rewards, total_num_steps):
        for agent_id in range(self.num_agents):
            for episode_id in range(5):
                    reward = rewards[episode_id][agent_id]
                    agent_k = "agent%i/" % agent_id + "reward/"+str(episode_id)
                    # if self.use_wandb:
                    #     pass
                    # wandb.log({agent_k: v}, step=total_num_steps)
                    # else:
                    self.writter.add_scalars(agent_k, {agent_k: reward}, total_num_steps)

    def log_action(self, actions, total_num_steps):
        for agent_id in range(self.num_agents):
            for episode_id in range(5):
                    action = actions[episode_id][agent_id]
                    agent_a1 = "agent%i/" % agent_id + "action1/"+str(episode_id)
                    agent_a2 = "agent%i/" % agent_id + "action2/"+str(episode_id)
                    # if self.use_wandb:
                    #     pass
                    # wandb.log({agent_k: v}, step=total_num_steps)
                    # else:
                    self.writter.add_scalars(agent_a1, {agent_a1: action[0],}, total_num_steps)
                    self.writter.add_scalars(agent_a2, {agent_a2: action[1],}, total_num_steps)

    def log_env(self, env_infos, total_num_steps):
        for k, v in env_infos.items():
            if len(v) > 0:
                # if self.use_wandb:
                #     wandb.log({k: np.mean(v)}, step=total_num_steps)
                # else:
                self.writter.add_scalars(k, {k: np.mean(v)}, total_num_steps)
